Philipp Bücher

The paper benchmarks methods for inferring transcription factor binding specificities using a large community challenge and finds deep learning models outperform traditional methods, though positional weight matrices are competitive.

EPIC challenges teams to predict transcription initiation from DNA sequence in non-model metazoans, aiming to assess generalizability beyond human and model species.

This study identifies DNA binding motifs for 98 human transcription factors using meSMiLE-seq, revealing methylation-sensitive binding preferences for some TFs.

The paper uses GHT-SELEX to show many human TFs have high intrinsic sequence specificity and complex DNA binding, often matching in vivo ChIP-seq results.

The paper evaluates cross-platform DNA motif discovery tools using 4,237 experiments to identify reliable transcription factor binding motifs and highlights challenges in motif detection.

The paper identifies genomic binding sites of 166 poorly characterized human transcription factors, revealing that some bind to dark matter DNA and others to regulatory regions.

The paper systematically determines the sequence specificity of 332 poorly characterized human transcription factors, identifying over 4,000 experiments and revealing tens of thousands of new TF binding sites.

The paper identifies and experimentally validates preferred downstream positions in human core promoters resembling the Drosophila DPE, showing strict spacing dependency on the Inr.

The paper investigates how opposing chromatin remodelers influence transcription initiation frequency and start site selection in budding yeast by analyzing their binding and nucleosome displacement activity.

The paper combines regulatory element activity, gene expression, and genetic variation data across cell types to show how chromatin structure influences regulatory networks and genetic effects.